Industry Loss Warranty Trigger Verifier using LangChain and LangGraph
Industry Loss Warranties depend on reliable, tamper-evident verification of physical and monetary catastrophe loss thresholds. You will build a stateful verification pipeline using LangChain and LangGraph to correlate multi-source weather sensor feeds with index loss reporting. Achieve zero false-positive trigger executions and sub-second validation latency across catastrophe scenarios.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a stateful multi-step agent workflow in LangGraph to verify Industry Loss Warranty (ILW) index triggers.
How work is evaluated
Assess trigger execution accuracy and verify absence of false positives across storm scenario test suite.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zero_false_positives
No contract triggering when actual industry loss is below threshold.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
validation_latency_ms
Time taken to complete graph evaluation in milliseconds • target: 500 • range: 0-1000
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Construct stateful agent graph workflows using LangGraph and LangChain
Implement multi-source verification logic for physical weather parameters
Correlate official industry loss index numbers (PCS, PERILS) with regional damage feeds
Ensure robust exception handling for contract trigger validation
Reference links and supporting material
Catastrophe event loss reports, historical PCS index updates, and weather sensor telemetry streams.
Challenge setup
Assess trigger execution accuracy and verify absence of false positives across storm scenario test suite.
Challenge input
JSON containing official industry loss report and physical sensor metadata
LangChain
Standardized prompt templates and tool wrapper interfaces.
LangGraph
Stateful graph execution with conditional logic and node recovery.
Evaluated output
JSON trigger evaluation decision with timing and verification metrics
- No contract triggering when actual industry loss is below threshold.
- Time taken to complete graph evaluation in milliseconds • target: 500 • range: 0-1000
- Benchmark: ILWVerificationBench-2026
- Validation Latency Ms target: 500
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
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Configured tools
- LangChain · Required
- LangGraph · Optional
- Gemini 2.5 Flash · Optional
- LangChain · Required
- LangGraph · Optional
Evaluation contract
- zero_false_positives · Weight 1
- validation_latency_ms · Weight 1
Metrics
- validation_latency_ms · Target 500
Benchmarks
- ILWVerificationBench-2026
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing official industry loss report and physical sensor metadata
Output: JSON trigger evaluation decision with timing and verification metrics
Execution harness: Python
Evaluation tools
- LangChain · Agent Framework
- LangGraph · State Orchestrator
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